Duty cycle estimation for job assignment

ABSTRACT

A computer implemented system is described for assigning executable jobs to pipeline sets, whereby the jobs may be network based computer jobs. The assigning includes generating a weight for each pipeline set of multiple pipeline sets to obtain multiple weights. Generating a weight includes obtaining duty cycle metrics for pipeline software threads in the pipeline set. The duty cycle metrics include a measure of an amount of time that a corresponding pipeline thread is executing and actively processing data. Generating the weight further includes determining the weight for the pipeline set based at least in part on the duty cycle metrics. The method further includes assigning a job request to a target pipeline set selected from the pipeline sets according to a weighted random algorithm, wherein the weighted random algorithm uses the weights.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of, and thereby claims benefit under35 U.S.C. § 120 to, U.S. patent application Ser. No. 17/070,110, filedon Oct. 14, 2020, which is incorporated herein by reference in itsentirety. U.S. patent application Ser. No. 17/070,110 is a continuationof, and thereby claims benefit under 35 U.S.C. § 120 to, U.S. patentapplication Ser. No. 16/399,773, filed on Apr. 30, 2019, which isincorporated herein by reference in its entirety.

BACKGROUND

Load distribution is the process of assigning of jobs to applicationthreads. A job is a unit of work for execution. For example, a job maybe a request that should be processed. Generally, load distribution maybe performed using a round robin algorithm. In the round robinalgorithm, jobs are assigned to the next thread in a circular order ofthreads.

BRIEF DESCRIPTION OF DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented.

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented.

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments.

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments.

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments.

FIG. 6 illustrates how a search query received from a client at a searchhead can split into two phases in accordance with a disclosedembodiment.

FIG. 7 illustrates a block diagram of an example cloud-based data intakeand query system in accordance with the disclosed embodiments.

FIG. 8 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments.

FIG. 9 illustrates a diagram of a software application in accordancewith one or more embodiments.

FIG. 10 illustrates a diagram of an application thread system inaccordance with one or more embodiments.

FIG. 11 illustrates a diagram of a data intake and query system forperforming ingest in accordance with disclosed embodiments.

FIG. 12 illustrates a diagram of a pipeline set in accordance withdisclosed embodiments.

FIG. 13 illustrates a diagram of an example pipeline set having examplepipelines in accordance with disclosed embodiments.

FIG. 14 illustrates a flowchart of an application thread generating aduty cycle metric in accordance with disclosed embodiments.

FIG. 15 illustrates a flowchart of assigning jobs to pipeline sets basedon duty cycle metrics in accordance with disclosed embodiments.

FIG. 16 illustrates a flowchart of gathering a duty cycle metric inaccordance with disclosed embodiments.

FIG. 17 illustrates a flowchart of generating pipeline set weights inaccordance with disclosed embodiments.

FIG. 18 illustrates a flowchart of using a periodic update model togenerate pipeline set weights in accordance with disclosed embodiments.

FIG. 19 illustrates an example interface in accordance with one or moreembodiments of the invention.

FIG. 20 illustrates an example interface in accordance with one or moreembodiments of the invention.

FIG. 21 illustrates an example interface in accordance with one or moreembodiments of the invention.

FIG. 22 illustrates an example interface in accordance with one or moreembodiments of the invention.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. However, it will be apparent toone of ordinary skill in the art that the invention may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, third,etc.) may be used as an adjective for an element (i.e., any noun in theapplication). The use of ordinal numbers is not to imply or create anyparticular ordering of the elements nor to limit any element to beingonly a single element unless expressly disclosed, such as by the use ofthe terms “before”, “after”, “single”, and other such terminology.Rather, the use of ordinal numbers is to distinguish between theelements. By way of an example, a first element is distinct from asecond element, and the first element may encompass more than oneelement and succeed (or precede) the second element in an ordering ofelements.

Further, although the description includes a discussion of variousembodiments, the various disclosed embodiments may be combined invirtually any manner. All combinations are contemplated herein.

In general, embodiments are directed to application thread managementusing duty cycle measurements. A duty cycle is the time that anapplication thread spends in active performance of processing data. Inother words, an application thread may use the central processing unit(CPU) and memory while still waiting for data to be processed. Thus, CPUand memory usage may incorrectly show that a thread is busy. One or moreembodiments use a duty cycle ownership object that starts tracking timeonly when data exists in a consumer queue for the thread and before thethread starts processing the data. In one or more embodiments, stoppingthe time is passively performed as the start of the duty cycle ownershipobject is scoped.

In one or more embodiments, a group of threads are grouped into a threadset. One or more embodiments determine the duty cycle metric for thethread set based on the duty cycle metric of threads in the thread set.Using the duty cycle metric for the thread set, the thread set may bemanaged.

By way of an example, one or more embodiments may be applied to pipelineprocessing of jobs. Threads may be grouped based on being threads of asame pipeline set. Jobs may be assigned to a pipeline set based on theduty cycle metric determined for the threads of the pipeline set andgenerating a duty cycle metric for the pipeline set. For example, thepipeline set may be an ingest pipeline that is configured to index andstore data into a data store. Multiple ingest pipeline sets may exist.In the ingest case, an ingest request may not be reflective of theamount of data to process by the ingest pipeline set and store in thedata store. Further, the amount of data may greatly vary between ingestrequests. Therefore, assigning ingest requests in a round robin stylemay not provide for balancing distribution of the ingested data. Byassigning using duty cycle metrics, one or more embodiments provide fora more balance use of hardware and software resources of a computingsystem.

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

-   -   2.1. Host Devices    -   2.2. Client Devices    -   2.3. Client Device Applications    -   2.4. Data Server System    -   2.5. Data Ingestion        -   2.5.1. Input        -   2.5.2. Parsing        -   2.5.3. Indexing    -   2.6. Query Processing    -   2.7. Field Extraction    -   2.8. Acceleration Techniques        -   2.8.1. Aggregation Technique        -   2.8.2. Keyword Index        -   2.8.3. High Performance Analytics Store        -   2.8.4. Accelerating Report Generation    -   2.9. Security Features    -   2.10. Data Center Monitoring    -   2.11. Cloud-Based System Overview    -   2.12. Searching Externally Archived Data        -   2.12.1. ERP Process Features    -   2.13. IT Service Monitoring    -   2.14. Cloud-Based Architecture        3.0. Workflow Management        4.0. Hardware

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, pre-definedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters forms a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5 ).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML, documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As an example, the client application may be aweb application that is served to and displayed in a web browser orother local application. As another example, a client application 110may comprise a mobile application or “app.” For example, an operator ofa network-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally, or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally, or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events and may alsoinclude one or more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equal sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an example process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIG.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “1” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 6 illustrates how a search query 602received from a client at a search head 210 can split into two phases,including: (1) subtasks 604 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 606 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 604, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4 , or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head aggregates the received results 806 to form asingle search result set. By executing the query in this manner, thesystem effectively distributes the computational operations across theindexers while minimizing data transfers.

2.8.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4 , data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.8.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user or canbe scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.8.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criterion, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.9. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

2.10. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

2.11. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one or more computing resources that are accessible to endusers over a network, for example, by using a web browser or otherapplication on a client device to interface with the remote computingresources. For example, a service provider may provide a cloud-baseddata intake and query system by managing computing resources configuredto implement various aspects of the system (e.g., forwarders, indexers,search heads, etc.) and by providing access to the system to end usersvia a network. Typically, a user may pay a subscription or other fee touse such a service. Each subscribing user of the cloud-based service maybe provided with an account that enables the user to configure acustomized cloud-based system based on the user's preferences.

FIG. 7 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2 , the networkedcomputer system 700 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system700, one or more forwarders 204 and client devices 702 are coupled to acloud-based data intake and query system 706 via one or more networks704. Network 704 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 702 and forwarders204 to access the system 706. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 706 forfurther processing.

In an embodiment, a cloud-based data intake and query system 706 maycomprise a plurality of system instances 708. In general, each systeminstance 708 may include one or more computing resources managed by aprovider of the cloud-based system 706 made available to a particularsubscriber. The computing resources comprising a system instance 708may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 702 to access a web portal or otherinterface that enables the subscriber to configure an instance 708.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 708) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD are centrally visible).

2.12. Searching Externally Archived Data

FIG. 8 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 804 over network connections820. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 8 illustrates thatmultiple client devices 804 a, 804 b, . . . , 804 n may communicate withthe data intake and query system 108. The client devices 804 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 8 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a system developer kit(SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 804 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 810. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 810, 812. FIG. 8 shows two ERP processes 810, 812 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 814 (e.g., Amazon S3, Amazon EMR, otherHadoop Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 816. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 810, 812indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively, or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 810, 812 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 810, 812 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 810, 812 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 810, 812 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices814, 816, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the results,or a processed set of results based on the returned results to therespective client device.

Client devices 804 may communicate with the data intake and query system108 through a network interface 820, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.12.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically, the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. One examplequery language is Splunk Processing Language (SPL) developed by theassignee of the application, Splunk Inc. The search head typicallyunderstands how to use that language to obtain data from the indexers,which store data in a format used by the SPLUNK® Enterprise system.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.13. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (IT SM)system, such as a system available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

2.14 Cloud-Based Architecture

As shown in the previous figures, various embodiments may refer to adata intake and query system 108 that includes one or more of a searchhead 210, an indexer 206, and a forwarder 204. In other implementations,data intake and query system 108 may have a different architecture, butmay carry out indexing and searching in a way that is indistinguishableor functionally equivalent from the perspective of the end user. Forexample, data intake and query system 108 may be re-architected to runin a stateless, containerized environment. In some of these embodiments,data intake and query system 108 may be run in a computing cloudprovided by a third party or provided by the operator of the data intakeand query system 108. This type of cloud-based data intake and querysystem may have several benefits, including, but not limited to,lossless data ingestion, more robust disaster recovery, and faster ormore efficient processing, searching, and indexing. A cloud-based dataintake and query system as described in this section may provideseparately scalable storage resources and compute resources, orseparately scalable search and index resources. Additionally, thecloud-based data intake and query system may allow for applications tobe developed on top of the data intake and query system, to extend orenhance functionality, through a gateway layer or one or moreApplication Programming Interfaces (APIs), which may providecustomizable access control or targeted exposure to the workings of dataintake and query system 108.

In some embodiments, a cloud-based data intake and query system mayinclude an intake system. Such an intake system can include, but is notlimited to an intake buffer, such as Apache Kafka® or Amazon Kinesis®,or an extensible compute layer, such as Apache Spark™ or Apache Flink®.In some embodiments, the search function and the index function may beseparated or containerized, so that search functions and index functionsmay run or scale independently. In some embodiments, data that isindexed may be stored in buckets, which may be stored in a persistentstorage once certain bucket requirements have been met, and retrieved asneeded for searching. In some embodiments, the search functions andindex functions run in stateless containers, which may be coordinated byan orchestration platform. These containerized search and indexfunctions may retrieve data needed to carry out searching and indexingfrom the buckets or various other services that may also run incontainers, or within other components of the orchestration platform. Inthis manner, loss of a single container, or even multiple containers,does not result in data loss, because the data can be quickly recoveredfrom the various services or components or the buckets in which the datais persisted.

In some embodiments, the cloud-based data intake and query system mayimplement tenant-based and user-based access control. In someembodiments, the cloud-based data intake and query system may implementan abstraction layer, through a gateway portal, an API, or somecombination thereof, to control or limit access to the functionality ofthe cloud-based data intake and query system.

3.0 Workflow Management

In general, workflow management is the assigning of jobs to applicationthreads in a manner that equalizes the distribution of the amount ofwork across threads. Because each job may involve an unequal amount ofwork, equal distribution of jobs may create a scenario of unequal amountof work. Thus, one or more embodiments use a duty cycle metric toestimate the business of application threads executing. The duty cyclemetric gathers information about the length of time during a period inwhich the application thread is performing useful work. Specifically,the duty cycle metric is a measure of an amount of time that acorresponding pipeline thread is executing and actively processing dataas opposed to taking an execution cycle to wait for data. In otherwords, the duty cycle metric is not a measure of central processing unit(CPU) or other hardware resource assigned to the application thread.

Turning now to FIG. 9 , FIG. 9 illustrates a diagram of a softwareapplication 902 in accordance with one or more embodiments. A softwareapplication 902 is any software program, such as an enterprise system, auser level application, an operating system, a security application, adatabase server, or any other application having multiple applicationthreads. The software application 902 may be or may execute within avirtual machine, execute on a server, execute on a user's computingdevice. By way of an example, the software application may be theSPLUNK® ENTERPRISE system, or a component thereof, described above withreference to FIGS. 1-8 . As shown in FIG. 9 , the software applicationmay include one or more thread set(s) 904, at least one aggregationthread 906, and an assigner 908. Each of these components is describedbelow.

In one or more embodiments, a thread set 904 is a group of applicationthreads 910 that operate together to process a job. A job is a unit ofwork for execution. For example, a job may be data or a request thatshould be processed. The amount of processing may vary greatly betweenjobs. For example, if the job is a data ingestion task to index andstore data in a data store, then the amount of processing is dependenton the amount of data to be stored. As another example, if the job is toprocess a query for data and transmit results, the amount of processingmay be dependent on the amount of data searched, the degree ofcomplication of the query (e.g., in terms of the number of data storesand correlations to perform, and other aspects of the query). In one ormore embodiments, each thread set is replica that is capable ofprocessing the same jobs as other thread sets. In other words, the samejob may be distributed to any of the thread sets. In some embodiments,the replication is only with respect to a portion of thread sets. Forexample, the thread sets may be partitioned into groups, wherein withina group of thread sets, the thread sets are replicas whereas a threadset may not be a replica of a thread set outside of the group.

By way of some examples of thread sets, a thread set may be managementthread sets, indexing thread sets, metadata thread sets, thread poolworker thread sets, etc. Management thread sets include one or moremanagement application threads that process management jobs for thesoftware application. Indexing thread sets include one or more indexingapplication threads that process jobs for indexing data in the datastores. Metadata thread sets include one or more application threadsthat process jobs for extracting and maintaining metadata. Thread poolworker thread sets include one or more application threads that processqueries. Other types of thread sets having other types of applicationthreads may be used without departing from the scope of the disclosure.

Continuing with the discussion of FIG. 9 , a thread set 904 may includemultiple application threads 910, producer consumer queues 912, statemachines 914 and event loops 916. An application thread 910 is a userlevel thread that is configured to process a portion of the job. Theapplication thread 910 may be represented by a program counter,register, execution stack, and control block. Multiple applicationthreads may exist in a thread set. Each application thread of a singlethread set is configured to process individual portions of the job. Inone or more embodiments, the individual portions are non-overlapping.

Application threads 910 may communicate with each other using producerconsumer queues 912. A producer consumer queue is a queue whereby afirst application thread (i.e., the producer) adds data elements to thequeue and a second application thread removes data elements from thequeue. Thus, the data elements are communicated between the first andthe second application thread. A state machines 914 is a construct thatdefines a finite set of states, the triggers causing transitions betweenstates, and maintains the current state within the finite set of states.An event loop 916 is a programming construct that waits for anddispatches events or messages in the software application. The eventloop issues a request to an internal or external “event provider” thatprovides events. The event loop then issues a call to the correspondingevent handler.

In one or more embodiments, the processing of the job by applicationthreads 910 may be dependent on a set of one or more conditions. Inother words, the application thread may not actively process jobs untilthe set of conditions is satisfied. The set of conditions at leastincludes the condition that the application thread has data for activelyprocessing the job. Other conditions may also exist that should also besatisfied in order to process the job. The set of conditions may includean event being issued from event loop 916, data in the producer consumerqueue 912, the application thread being in a particular state in thestate machine 914, the operating system schedule assigning executioncycles to the application thread, or other condition.

The thread sets 904 are connected to at least one aggregation thread906. An aggregation thread 906 is a thread that obtains duty cyclemetrics from the application threads in the thread sets 904 and assignsweights to the thread sets 904. For example, the aggregation thread 906may access stored information, generate a duty cycle metric for eachapplication thread. The duty cycle metric is a measure of the degree inwhich the corresponding application thread is processing data. Theaggregation thread 906 may further use the duty cycle metric for eachapplication thread in a thread set to generate a thread set duty cyclemetric for the thread set. The thread set duty cycle metric is a valuethat defines the degree to which the thread set as a whole is processingthe job. In other words, whereas each job may have varying amounts ofprocessing to perform such that the number of jobs assigned to a threadset does not defined how busy the thread set is, the thread set dutycycle metric does define how busy the corresponding thread set is. Inone or more embodiments, the aggregation thread 906 is configured togenerate weights for the thread sets. The weight is a value that definesa relative measure of the degree in which the corresponding applicationthread is processing data as compared to other thread sets. In one ormore embodiments, the weights are normalized values, such as values on ascale between 0 and 1. The operations by the aggregation thread 906 isdescribed in further detail in FIGS. 16-18 .

Continuing with FIG. 9 , an assigner 908 is communicatively connected tothe aggregation thread 906 and the thread sets 904. The assigner 908includes functionality to assign jobs to the thread sets 906 based onthe weights. In one or more embodiments, the assigner assigns jobs basedon a weighted random distribution, whereby the probability that a threadset is randomly selected for a job is defined by the weight assigned tothe thread set. By way of an example, the assigner 908 may implementVose's alias method to apply a weighted random distribution and assignjobs based on the weighted random distribution.

Continuing with the discussion, FIG. 10 illustrates a diagram of anapplication thread system in accordance with one or more embodiments.Specifically, FIG. 10 shows a system diagram of at least some componentsthat interface with application thread 1002. As shown in FIG. 10 , theapplication thread 1002 may interface with a state machine 1006, anevent loop 1008, and producer consumer queues (e.g., consumer queue1004, producer queue 1010). The state machine 1006 and event loop 1008are the same as the state machine and event loop described above withrespect to FIG. 9 . The consumer queue 1004 is the same as the producerconsumer queue described above with the respect to FIG. 9 . Likewise,the producer queue 1010 is the same as the producer consumer queuedescribed above with the respect to FIG. 9 . The consumer queue 1004 isreferred to as a consumer queue with respect to the application threadbecause the application thread 1002 consumes (e.g., receives as input)data from the consumer queue 1004. Conversely, because the applicationthread 1010 produces (e.g., as output) data to the producer queue 1010,the producer queue 1010 is referred to as a producer queue with respectto the application thread. The producer consumer queues may be stored inmemory, on disk, or any other storage. Some application threads may notinclude a producer queue and/or consumer queue. For example, if anapplication thread is directly obtaining data from a data store or froma network, then the application thread may not have a consumer queue.Likewise, if the application thread is storing data to a data store ornetwork, then the application thread may not have a producer queue.

The duty cycle ownership object 1012 is a software object that isconfigured to track the duty cycle metrics for the application thread1002. In one or more embodiments, the duty cycle ownership object isseparate from the application thread. A duty cycle object is related toa single application thread 1002. Further, in one or more embodiments,an application thread is related to a single duty cycle object. Therelationship is through an ownership claim. In one or more embodiments,each application thread in the thread set is related to a correspondingduty cycle object. Once ownership is claimed by the application thread1012 for the duty cycle object, the duty cycle object is used to store,in an application thread specific stack, timestamps and current state ofprocessing. The timestamps and current state are used to generate theduty cycle metric for the application thread. Storing timestamps andcurrent state is described below with reference to FIG. 14 . In someembodiments, rather than storing timestamps, stage changes are stored,and a weighted average is updated.

FIG. 11 illustrates a diagram of a data intake and query system forperforming ingest in accordance with disclosed embodiments.Specifically, FIG. 11 shows a diagram for performing duty cycle basedjob assignment to assign ingest jobs in the data intake and query system108 described above with reference to FIGS. 1 and 2 . For the purposesof simplifying the description, any number of forwarders 204, indexers206, and data stores 208 may exist. Embodiments are not limited to theconfiguration shown in FIG. 11 .

An ingest job is a job to index and store incoming data from at leastone data source (not shown) in a data store. In one or more embodiments,an ingest job is performed by pipeline sets on the indexers 206. Ingeneral, a pipeline set is a thread set (e.g., thread set 904 in FIG. 9) that executes a pipeline. Application threads are sequentially orderedin the pipeline, such that each application thread performs theoperations of a portion of the pipeline before passing the data to thenext application thread in the pipeline. Application threads in apipeline set may be referred to as pipeline threads. FIG. 12 shows apipeline set. An example of a pipeline set is described below withreference to FIG. 13 . Although pipeline sets are described with respectto ingest requests, pipeline sets may be used for other operations, suchas query processing.

Continuing with FIG. 11 , an aggregation thread 1106 is a thread that isat least configured to aggregate the duty cycle metrics from thepipeline threads (i.e., pipeline thread duty cycle metrics) in thepipeline sets to generate duty cycle metrics for pipeline sets (i.e.,pipeline set duty cycle metrics). The aggregation thread 1106 mayperform the same functionality as aggregation thread 906 described withreference to FIG. 9 . The ingest assigner 1104 is at least one threadthat is configured to assign at least ingest jobs to pipeline sets 1102.The ingest assigner 1104 may perform the same functionality as describedwith respect to the assigner 908 described with reference to FIG. 9 .

Although not shown in FIG. 11 , a similar set of components of theindexer 206 in FIG. 11 may exist on other portions of the data intakeand query system 108 to process requests. The operations to obtain dutycycle metrics and assign jobs may be the same on the other components ason the indexer 206. For example, the forwarder 204 may have multiplepipeline sets, an assigner and an aggregation thread. As described withreference to FIG. 2 , the operations performed by the pipeline sets toprocess ingest requests is different on the forwarder 204 than on theindexer 206. The operations to obtain duty cycle metrics and assign jobsmay be the same on the forwarder 204 as on the indexer 206.

Further, although not shown in FIG. 11 , similar operations may beperformed by a cluster master. A cluster master is software and/orhardware that is configured to manage a cluster of computing nodes. Forexample, a cluster master may manage virtual machines executing portionsof the data intake and query system. The management by the clustermaster may be to add and remove computing nodes, and to load balancejobs among the computing nodes. Aggregation threads (e.g., aggregationthread 1106) in the data intake and query system may report (i.e.,through transmission or storage) the duty cycle metrics directly orindirectly to the cluster master. The cluster master may use thecollected metrics to generate weights for indexers and forwarders wouldload balance incoming ingestion requests to the indexers based on theweights. If the indexers and forwarders all have low weights (i.e.,indicating that the indexers and forwarders are loaded with jobs), thecluster master may add computing nodes. The cluster master may alsoremove computing nodes if the indexers and forwarders are not loadedwith jobs.

FIG. 12 illustrates a diagram of a pipeline set 1102 in accordance withdisclosed embodiments. As shown in FIG. 12 , the pipeline set 1102includes pipeline threads 1202. Each pipeline thread 1202 includes adiscrete unit of work for the pipeline set 1102 before passing the datato the next pipeline thread. As designated by arrows 1204, more than twopipeline threads may exist, whereby the pipeline threads are ordered insequential order. The sequential order may also be a consecutive order.Once a pipeline thread processes a data element, the pipeline threaddoes not process the data element or derivatives of the data element(i.e., the pipeline thread is not in multiple positions in thepipeline).

In one or more embodiments, each pipeline thread executes an individualpipeline. Each individual pipeline is a sequence of operations in whichthe performance of operation is dependent on a prior operation. As shownin FIG. 12 , each pipeline thread 1102 is related to a unique duty cycleownership object 1206. The duty cycle ownership object is the same orsimilar to the duty cycle ownership object 1012 described above withreference to FIG. 10 .

FIG. 13 illustrates a diagram of an example pipeline set 1302 havingexample pipelines (i.e., parsing pipeline 1304, merging pipeline 1306,typing pipeline 1308, and index pipeline 1310) to process incomingingest requests in accordance with disclosed embodiments. Each pipeline(i.e., parsing pipeline 1304, merging pipeline 1306, typing pipeline1308, and index pipeline 1310) has an individual and unique pipelinethread assigned to the pipeline. For example, the pipeline thread forthe parsing pipeline 1304 performs UTF-8 (i.e., Unicode TransformationFormat-8) operations 1312 to identify individual characters, linebreaker analysis 1314 on the individual characters to identifyindividual lines, and header analysis 1316 to relate the lines toattributes. The pipeline thread for the merging pipeline 1306 mayperform various aggregator operations. the pipeline thread for thetyping pipeline 1308 performs regular expression (Regex) replacementoperations 1320 and annotator operations 1322. The pipeline thread forthe index pipeline 1310 performs transport control protocol (TCP) outoperations 1324, system log out operations 1326 to write to a systemlog, and indexer operations 1328 to write to the index.

Continuing with the example, between the pipelines are producer consumerqueues (i.e., parsing queue 1330, agg queue 1332, typing queue 1334, andindex queue 1336) that provide a communication structure for pipelinethreads to provide data to each other. The parsing queue 1330 is aproducer queue for incoming data received by the example pipeline set1302. The parsing queue 1330 is a consumer queue for the pipeline threadof the parsing pipeline 1304. The agg queue 1332 is a producing queuefor the pipeline thread of the parsing pipeline 1304 and a consumerqueue for the pipeline thread of the merging pipeline 1306. In otherwords, the parsing pipeline thread places data in the agg queue 1332,which is removed by the merging pipeline thread. The typing queue 1334is a producing queue for the pipeline thread of the merging pipeline1306 and a consumer queue for the pipeline thread of the typing pipeline1308. Thus, the merging pipeline thread places data in the typing queue1334, which is removed by the typing pipeline thread. The index queue1336 is a producing queue for the pipeline thread of the typing pipeline1308 and a consumer queue for the pipeline thread of the index pipeline1310. Accordingly, the typing pipeline thread places data in the indexqueue 1336, which is removed by the index pipeline thread. Although notshown, the index pipeline thread may place data in a queue to be writtento the data store.

Turning now to the flowcharts, the FIGS. 14-18 shows flowcharts for jobload distribution based on duty cycle. The steps of FIGS. 14-18 may beperformed using the various components of the system described above.While the various steps in these flowcharts are presented and describedsequentially, one of ordinary skill will appreciate that some or all ofthe steps may be executed in different orders, may be combined oromitted, and some or all of the steps may be executed in parallel.Furthermore, the steps may be performed actively or passively. Forexample, some steps may be performed using polling or be interruptdriven in accordance with one or more embodiments of the disclosure. Byway of an example, determination steps may not require a processor toprocess an instruction unless an interrupt is received to signify thatcondition exists in accordance with one or more embodiments of theinvention. As another example, determination steps may be performed byperforming a test, such as checking a data value to test whether thevalue is consistent with the tested condition in accordance with one ormore embodiments of the disclosure.

FIG. 14 illustrates a flowchart of an application thread generating aduty cycle metric in accordance with disclosed embodiments. At block1402, an application thread claims ownership of the duty cycle ownershipobject and start the duty cycle ownership object in an off state. Theduty cycle ownership object may be a software object existing intransient storage. The application thread may claim ownership of itsper-thread duty cycle object thereby informing the system that thisthread has been instrumented and is available for duty cycle metricscollection. When the application thread claims ownership of the dutycycle ownership object, the duty cycle metrics are in an off state. Thestate of the duty cycle ownership object indicates whether theapplication thread is currently actively processing data. The state maybe an off state or an on state. In the off state, the conditions do notexist for the application thread to actively process data. As such, theapplication thread is deemed not currently busy for the purposes ofdetermining duty cycle. In the on state, the conditions exist for theapplication thread to process data. Accordingly, the application threadis deemed currently busy for the purposes of determining duty cycle.

At Block 1404, the consumer queue of the application thread isinitialized. For example, the consumer queue may be related to theapplication thread.

At Block 1406, a determination is made whether a set of conditions issatisfied. The set of conditions includes one or more conditions thatall are satisfied for the set to be satisfied in one or more embodimentsof the disclosure. The set of conditions includes that the applicationthread is able to actively process data. Specifically, activelyprocessing data means that the application thread is not waiting, suchas for another application thread or an event to occur. The set ofconditions further include the application thread being assignedexecution cycles. In one or more embodiments, the set of conditionsinclude a data object being in the consumer queue of the applicationthread. If the data object is in the consumer queue, then theapplication thread has data to process. Testing whether the set ofconditions is satisfied may be performed by an if statement executed bythe application thread. If the set of conditions is not satisfied, thenthe application thread waits as shown by the no loop. The applicationthread waiting may be independent of whether execution cycles areassigned to the application thread. For example, the operating systemmay repetitively assign execution cycles to the application thread evenwhen the application thread does not have data to process. The executioncycles may be used just to test whether the application thread has datato process. Because execution cycles are assigned to a waiting thread atleast to test whether data exists, measuring execution cycles is not ameasurement of duty cycle.

At Block 1408, if the set of conditions is satisfied, the state of theduty cycle ownership object is set to the on state. In one or moreembodiments, the on state is set in an execution stack for theapplication thread. In one or more embodiments, a timestamp is recorded.In one or more embodiments, the setting of the duty cycle ownershipobject to the on state is scoped to the conditional statement testingwhether the set of conditions is satisfied. For example, the timestampmay be recorded in the execution stack at the location of theconditional statement. Further, instructions may be added to theexecution stack such that at the end of the conditional statement, theduty cycle ownership object is switched to an off state.

At Block 1410, the application thread processes the data object in theconsumer queue. Specifically, the application thread actively executesthe operations of the application thread to process data. If theapplication thread is a pipeline thread, then the pipeline threadperforms the portion of the pipeline assigned to the pipeline thread.

At Block 1412, the scope of the set of conditions being satisfied isexited changing the duty cycle ownership object to an off state. At theend of the execution, the duty cycle ownership object is changed to anoff state. A timestamp is obtained for the change at the end of thescope.

In some embodiments, the timestamp may be explicitly or implicitlyrelated in storage to the transition to off state. In such embodiments,the aggregation thread aggregates the amount of time that the duty cycleownership object is in an on state based on multiple timestamps in aperiod.

In some embodiments, the application thread calculates the differencebetween the timestamp obtained as part of Block 1404 and a currenttimestamp when the duty cycle ownership object is switched to an offstate. The application thread may record the difference for theaggregation thread. At the end of the period, the aggregation thread maycalculate a total of the time that the duty cycle ownership object is inan on state from the set of differences for a period. As anotherexample, the application thread may record a running total of thedifference for the aggregation thread by summing the current calculateddifference between timestamps with a previous total. The aggregationthread may use the single running total to obtain the duty cycle metric.

As described in FIG. 14 , the application thread stores data from whichthe duty cycle metric may be derived for a period. At the end of aperiod or a collection of periods, weights may be reassigned to assignjobs. FIG. 15 illustrates a flowchart of assigning jobs to pipeline setsbased on duty cycle metrics in accordance with disclosed embodiments.While FIGS. 15-18 are described with reference to pipeline sets, thesame or similar techniques may be performed for assigning jobs to othertypes of thread sets.

Initially, jobs are assigned with each pipeline set having equalweights. For example, weighted round robin may be used, or a weightedrandom distribution may be used, whereby the weights are the same acrosspipeline threads.

At Block 1502, a weight for each pipeline set is generated. Theaggregation thread generates the weight in one or more embodiments.Generating the weight includes gathering pipeline thread duty cyclemetrics and calculating the weight from the pipeline thread duty cyclemetrics. In one or more embodiments, the aggregation thread obtains thedata, described above with reference to FIG. 14 , stored by the pipelinethread from which the pipeline thread duty cycle metric may bedetermined. For a pipeline set, the aggregation thread may combine thepipeline thread duty cycle metrics of pipeline threads in the pipelineset to generate a pipeline set duty cycle metric. The aggregation threadmay use the pipeline set duty cycle metric to generate the weight forthe pipeline set.

At Block 1504, a job is assigned to a pipeline set according to aweighted random algorithm that uses the assigned weights. In one or moreembodiments, Vose's alias method is used to generate a list having theweighted random distribution of selecting pipeline sets. The assigner ora different thread may use Vose's alias method. When a job is receivedfor assignment, the assigner may select a next pipeline set in the listto obtain the selected pipeline set. The assigner assigns the jobrequest to the selected pipeline set. As another example, applying theweighted random distribution may be performed on demand as new jobrequests are received.

Assigning a job to a pipeline set may be performed by performing one ormore of the following operations. In storage, a pipeline set identifierof a selected pipeline set may be related to a job identifier of thejob. An event may be issued to the selected pipeline set with the jobidentifier of the job. The job identifier may be stored a job queue ofthe selected pipeline set, whereby the job queue is accessed by theselected pipeline set. The job may be transmitted to the selectedpipeline set.

FIG. 16 illustrates a flowchart of gathering duty cycle metrics inaccordance with disclosed embodiments. In particular, FIG. 16 providesan example technique for performing Step 1502 of FIG. 15 . At Block1602, for each pipeline thread, a duty cycle metric is determined basedon data obtained from the duty cycle ownership object. The duty cyclemetric is a measure of the percentage of time that the duty cycleownership object is in an on state as compared to an off state. Ratherthan being a percentage, the duty cycle metric may be a total. Becausethe length of the period is the same across pipeline sets, the amount oftime that the pipeline set duty cycle metric is an off state and thelength of the period may be ignored when calculating the weight.

In embodiments in which the application thread stores a collection oftimestamps, at the end of the period, the aggregation thread maydetermine the amount of time that the duty cycle ownership object is inthe on state as compared to the off state by calculating the differencesbetween consecutive timestamps. The aggregation thread may generate aweighted moving average for the period based on the differences in time.The weighted moving average may have different weights within the periodand/or between periods. For example, within a period, the weightedmoving average gives greater weight to the state of the latesttransitions and corresponding timestamps in the period. In someembodiments, the weighted moving average is weighted between periods.For example, the total time that the duty cycle ownership object is inthe on state (e.g., based on the differences between consecutive timemay be calculated for the period and divided by the length of theperiod.

In embodiments in which the application thread determines the amount oftime that the duty cycle metric is in the on state and stores the amountor amounts, the aggregation thread may use the amount to calculate thepipeline thread duty cycle metric. Specifically, the aggregation threadmay obtain the amounts from storage of the application thread. Theaggregation thread may use the difference or differences calculated bythe application thread to calculate the weighted moving average, asdescribed above. The weighted moving average is the pipeline set dutycycle metric in accordance with one or more embodiments.

Thus, the result of Block 1602 is a pipeline thread duty cycle metric.

At Block 1604, for at least a subset of the pipeline sets, the pipelineset duty cycle metric is set as the maximal pipeline thread duty cyclemetric of the pipeline set. In a pipeline set, the pipeline thread dutycycle metrics are obtained and compared with each other. The pipelinethread duty cycle metric having the maximal value is selected as thepipeline set duty cycle metric. The process is repeated for eachpipeline set in at least the sub set.

Although Block 1604 describes using a maximal pipeline thread duty cyclemetric as the pipeline set duty cycle metric, other statistic may beused. For example, the average, the median, or a different measureacross the pipeline thread duty cycle metrics for a pipeline set may beused.

Continuing with FIG. 16 , at Block 1606, a sliding window average numberof jobs to the pipeline sets is generated. A sliding window average isan average of the current period and the previous N periods, where N isa set number. One technique for generating the average number of jobs isto multiply the previous period computed average number of jobs by((N−1)/N) to obtain a first intermediate value, multiply the currentperiod number of jobs by (1/N) to obtain a second intermediate value,and add the first intermediate value to the second intermediate value.Other techniques for generating the average number of jobs may be usedwithout departing from the scope of the disclosure.

At Block 1608, the average and the pipeline set duty cycle metrics areused to manage at least the subset of pipeline sets. In one or moreembodiments, based on the average number of jobs and the pipeline setduty cycle metrics, weights are generated for the pipeline sets. Theaverage number of jobs is used to determine whether to apply equalweights or whether to vary the weights between pipeline sets. Thepipeline set duty cycle metrics are used to determine the relativeworkload of the pipeline thread sets as compared to other pipelinethread sets. Based on the relative workload, weights may be determinedin which the weights are inversely assigned to the workload. The greaterthe workload, the lower the weight. An example for generating thepipeline set weights using the average and the pipeline set duty cyclemetrics are described below with reference to FIG. 17 .

FIG. 17 illustrates a flowchart of generating pipeline set weights inaccordance with disclosed embodiments. At Block 1702, the sliding windowaverage number of jobs to the pipeline thread sets is obtained. Thesliding window average number of jobs is obtained as described abovewith reference to FIG. 16 . At Block 1704, a determination is madewhether the average number of jobs is equal to zero. If the averagenumber of jobs is equal to zero, then a determination is made thatinsufficient number of jobs exist, then the weights are assigned basedonly on the current period load metrics. Specifically, at Block 1706,the sum of the pipeline set duty cycle metrics is calculated across thepipeline sets. In other words, for the pipeline sets to which a job maybe assigned, the pipeline set duty cycle metrics are totaled.

At Block 1708, for each pipeline set, an assigned value is determined asa difference between the sum and the pipeline set duty cycle metric forthe pipeline set. For a pipeline set, the difference between the sumdetermined at Block 1704 and the pipeline set duty cycle metric of thepipeline set is calculated. The difference is the assigned value to thepipeline set. Obtaining the difference is performed for each pipelineset.

Returning to Block 1704, if the average number of jobs is greater thanzero, then the flow proceeds to Block 1710. At Block 1710, a periodicupdate model is used to assign assigned values to the pipeline sets. Theperiodic update model considers the current period as well as previousperiods when assigning values. An example technique for applying theperiodic update model is described below with reference to FIG. 18 .

Returning to FIG. 17 , the assigned values are normalized to obtainpipeline set weights. In one or more embodiments, normalizing theassigned values is transforming the assigned values to a scale, such asbetween zero and one, inclusive. For example, the assigned values may besummed to obtain a summation. The assigned value of a pipeline set maybe divided by the summation to obtain the corresponding weight for thepipeline set.

FIG. 18 illustrates a flowchart of using a periodic update model togenerate pipeline set weights in accordance with disclosed embodiments.In one or more embodiments, FIG. 18 describes a modified version ofDahlin's model to generate the pipeline set weights.

At Block 1802, pipeline sets are ordered in increasing based on thepipeline set duty cycle metrics to obtain ordered list. In the order,the pipeline set having the lowest pipeline set duty cycle metric is atthe starting position in the ordered list and the pipeline set havingthe greatest pipeline set duty cycle metric is last. The remainingpipeline sets are added to the ordered list so as to be in monotonicallyincreasing order of the pipeline sets' corresponding pipeline set dutycycle metrics. Thus, pipeline sets are ordered according to theirrespective workloads, such that the pipeline set having the greatestworkload is last.

At Block 1804, an incrementor variable X is initialized to one. X isused to mark a current position within the ordered list. Initially, isset to the starting position in the ordered list.

At Block 1806, a pipeline set is selected from position X of the orderedlist. The sum over i, from i at starting position to i at position X−1,of the pipeline set duty cycle metric at position X−1 minus pipeline setduty cycle metric at position i is calculated at Block 1808. Forexample, if X is 5, then the sum of differences between pipeline setduty cycle metrics at position 4 and positions 0-4 is calculated. AtBlock 1810, a determination is made whether the sum is greater than orequal to the average number of j obs. If the sucm is not greater than orequal to the average, the flow proceeds to Block 1812, whereby X isincremented by 1 and the process repeats starting with Block 1806.

Blocks 1806-1810 may be performed by calculating the following Equation(Eq. 1) below.Σ_(i=0)^(X−1)(DutyCycleShare_(X−1)−DutyCycleShare₁)<average_injection_rate  (Eq.1)

In Equation (Eq. 1), i is an incrementor, the DutyCycleShare_(X−1) isthe duty cycle metric of the pipeline set at position X−1, andDutyCycleShare_(i) is the duty cycle metric of the pipeline set atposition i. Average_injection_rate is the average number of jobsassigned for the last N periods.

If the sum is greater than or equal to the average, the flow proceeds toBlock 1814. At Block 1814, the assigned value is set to zero for eachpipeline set at a position in the ordered list that is greater than orequal to X. In other words, for any pipeline set with a greater pipelineset duty cycle metric than the pipeline set at position X, the assignedvalue and correspondingly the weight, may be zero.

At Block 1816, for each pipeline set at position less than X in orderedlist, the pipeline set is assigned an assigned value calculated as afunction of a difference between a duty cycle metric of the pipeline setand a duty cycle metric at the maximal position (i.e., position X), arate adjustment, and the sliding window average. The rate adjustmentaccounts for the average number of jobs of the pipeline set for the pastN periods. In one or more embodiments, the assigned values may becalculated using Equations (Eq. 2), (Eq. 3), and (Eq. 4) below.

$\begin{matrix}{{Pi} = {{\frac{{share} + {{arrival\_ rate}{\_ adjustment}}}{{average}{injection}{rate}}{for}i} < X}} & \left( {{Eq}.2} \right)\end{matrix}$share=DutyCycleShare_(X)−DutyCycleShare_(i)  (Eq. 3)

$\begin{matrix}{{{arrival\_ rate}{\_ adjustment}} = \frac{{{average\_ injection}{\_ rate}} - {\sum_{j = 0}^{X - 1}{DC}_{X - 1}} - {DC}_{j}}{{average\_ injection}{\_ rate}}} & \left( {{Eq}.4} \right)\end{matrix}$

In Equations (Eq. 2), (Eq. 3), and (Eq. 4), P₁ is the assigned value ofthe pipeline set at position i in the ordered list. TheDutyCycleShare_(X) is the duty cycle metric of the pipeline set atposition X, and DutyCycleShare_(i) is the duty cycle metric of thepipeline set at position i. Arrival rate adjustment is the rateadjustment. The arrival rate adjustment is a function of the averageinjection rate (e.g., the average number of jobs assigned for the last Nperiods). The average injection rate may be the value calculated abovein Block 1606 of FIG. 16 . DC_(X−1) is the duty cycle metric at positionX−1, DC_(j) is the duty cycle metric at position j, where j is anincrementing variable.

By way of an example of FIG. 18 , consider the scenario in which theduty cycle metrics are 0.1, 0.1, 0.1, 1 and average ingestion rate is 2.In the example, X is 4 because 1−0.1+1−0.1+1−0.1=2.7 and 2.7>2. Theweights are 33.34, 33.33, 33.33, 0. Thus, the load may be distributed onthe pipeline sets that are very lightly loaded (i.e., the first threepipeline sets in the list) in the next period.

While the above is one set of equations for assigning values to apipeline set, other equations may also be used without departing fromthe scope of the disclosure.

As described above, the duty cycle metrics may be used to assignpipeline sets to jobs. Application administrators may view metricsgathered through the application and regarding the assignment of jobsand configure the aggregator and assigner. In some embodiments, theapplication may be configurable to switch between using weighted roundrobin and weighted random distribution using duty cycle metrics. FIGS.19 and 20 illustrates an example administer interface showing weightedround robin being implemented. FIGS. 20 and 21 illustrate the exampleinterface showing weighted random distribution being implemented. In theexample, three pipeline sets are shown. The pipeline sets are pipelineset 0, pipeline set 1 and pipeline set 2 (denoted as 0, 1, and 2 in theexample interface Figures).

FIG. 19 illustrates an example round robin interface 1900 in accordancewith one or more embodiments of the invention. Graph 1902 is a timeseries graph of thread activity. Specifically, graph 1902 shows thebusiest pipeline by pipeline set. The graph shows the maximum pipelinethread activity of the busiest ingestion pipeline grouped by pipelineset. The busiest ingestion pipeline corresponds to the ingestionpipeline with the highest thread activity during the measurement period.The horizontal axis is time and the vertical axis is the pipeline setduty cycle metric. In other words, for the pipeline thread having themaximal duty cycle metric in the corresponding pipeline set, thevertical axis shows the pipeline thread activity (e.g., the percentageof maximum workload of the pipeline thread being used at the moment intime).

Graph 1904 is a time series graph of assignment values. In other words,graph 1904 is the ingestion assignment probability per pipeline set. Thegraph shows the relative pipeline set selection probably for newingestion assignment requests. The probability is a factor of thepipeline set selection policy shown in the indexing overview chart. Thevertical axis represents the distribution of the probability ofselecting a particular pipeline set. The horizontal axis is over time.As shown by the equal size blocks for pipeline sets 0, 1, and 2 overtime, the round robin used assigns equal weights to each pipeline set.As such, the Graph 1904 remains the same over time.

Graph 1906 is a time series graph of ingestion assignments.Specifically, graph 1906 is the ingestion assignments per pipeline set.The graph shows the number of ingestion assignments grouped by pipelineset. The ingestion assignments correspond to each new ingestionconnection request to the indexer. Again, the horizontal axis is timeand the vertical axis is the total number of ingestion assignments.Along the vertical axis, pipeline sets are color encoded to show thepercentage of the total number for each pipeline set. Again, becauseround robin is used, jobs are equally distributed to each pipeline set.

Next, comparing Graph 1902 with Graph 1906, even though jobs wereequally distributed to pipeline sets, the pipeline sets did not haveequal distribution of work. For example, pipeline set 1 had a greaterduty cycle measurement between time 12:18 and 12:26, than pipeline set 0and 2. Thus, the equitable distribution of jobs does not result in anequitable distribution of processing.

FIG. 20 illustrates an example interface 2000 when round robin isselected in 2002 in accordance with one or more embodiments of theinvention. Graph 2004 shows a time series graph of the duty cycle metricon a per pipeline thread basis. Specifically, Graph 2004 shows theaverage thread activity of the threads. Each thread is represented by adifferent line on the graph. Graph 2008 shows the average pipelinethread duty cycle metric grouped by pipeline set. In other words, thehorizontal axis is the duty cycle metric. The vertical axis is thepipeline set. Within the pipeline set, twelve pipeline threads areshown. The twelve pipeline threads show the percentage of the totalactivity is from each of the twelve respective pipeline threads. Asshown, the pipeline threads may not have proportional activity to thenumber of jobs or the percentage of the total activity.

Graph 2010 shows the busiest ingestion pipeline per pipeline set.Specifically, Graph 2010 shows the count of the busiest ingestionpipeline incidents grouped by pipeline set. The busiest ingestionpipeline corresponds to the ingestion pipeline with the highest threadactivity during the measurement period.

FIG. 21 and FIG. 22 illustrate similar interfaces as FIG. 19 and FIG. 20, but using weighted random distribution. FIG. 21 illustrates an exampleinterface in accordance with one or more embodiments of the invention.Graph 2102 is a time series graph of thread activity. Specifically,graph 2102 shows the busiest pipeline by pipeline set. The graph showsthe maximum pipeline thread activity of the busiest ingestion pipelinegrouped by pipeline set. The busiest ingestion pipeline corresponds tothe ingestion pipeline with the highest thread activity during themeasurement period. The horizontal axis is time and the vertical axis isthe pipeline set duty cycle metric. In other words, for the pipelinethread having the maximal duty cycle metric in the correspondingpipeline set, the vertical axis shows the pipeline thread activity(e.g., the percentage of maximum workload of the pipeline thread beingused at the moment in time).

Graph 2104 is a time series graph of assignment values. In other words,graph 1904 is the ingestion assignment probability per pipeline set. Thegraph shows the relative pipeline set selection probably for newingestion assignment requests. The probability is a factor of thepipeline set selection policy shown in the indexing overview chart. Thevertical axis represents the distribution of the probability ofselecting a particular pipeline set. The horizontal axis is over time.As shown by the variable size blocks for pipeline sets 0, 1, and 2 overtime, the weighted random distribution assigns unequal weights to eachpipeline set. Thus, when a pipeline set duty cycle metric indicates apipeline set is busy, the weight of the pipeline set is reduced. Theresulting changes is shown by a comparison of Graph 2102 with the Graph2104. Thus, as shown in Graph 2104, when pipeline set 1 is busy at time12:41 in Graph 2102, the weight is reduced.

Graph 2106 is a time series graph of ingestion assignments.Specifically, graph 2106 is the ingestion assignments per pipeline set.The graph shows the number of ingestion assignments grouped by pipelineset. The ingestion assignments correspond to each new ingestionconnection request to the indexer. Again, the horizontal axis is timeand the vertical axis is the total number of ingestion assignments.Along the vertical axis, pipeline sets are color encoded to show thepercentage of the total number for each pipeline set. Because differentweights are assigned, the distributions of jobs are not equal.

FIG. 22 illustrates an example interface 2200 in accordance with one ormore embodiments of the invention. when round robin is selected in 2202in accordance with one or more embodiments of the invention. Graph 2204shows a time series graph of the duty cycle metric on a per pipelinethread basis. Specifically, Graph 2204 shows the average thread activityof the threads. Each thread is represented by a different line on thegraph. Graph 2208 shows the average pipeline thread duty cycle metricgrouped by pipeline set. In other words, the horizontal axis is the dutycycle metric. The vertical axis is the pipeline set. Within the pipelineset, twelve pipeline threads are shown. The twelve pipeline threads showthe percentage of the total activity is from each of the twelverespective pipeline threads. As shown, the pipeline threads may not haveproportional activity to the number of jobs or the percentage of thetotal activity.

Graph 2210 shows the busiest ingestion pipeline per pipeline set.Specifically, Graph 2210 shows the count of the busiest ingestionpipeline incidents grouped by pipeline set. The busiest ingestionpipeline corresponds to the ingestion pipeline with the highest threadactivity during the measurement period.

4.0 Hardware

The various components of the figures may be a computing system orimplemented on a computing system. For example, the operations of thedata stores, indexers, search heads, host device(s), client devices,data intake and query systems, data sources, external resources, and/orany other component shown and/or described above may be performed by acomputing system. A computing system may include any combination ofmobile, desktop, server, router, switch, embedded device, or other typesof hardware. For example, the computing system may include one or morecomputer processors, non-persistent storage (e.g., volatile memory, suchas random access memory (RAM), cache memory), persistent storage (e.g.,a hard disk, an optical drive such as a compact disk (CD) drive ordigital versatile disk (DVD) drive, a flash memory, etc.), acommunication interface (e.g., Bluetooth interface, infrared interface,network interface, optical interface, etc.), and numerous other elementsand functionalities. The computer processor(s) may be an integratedcircuit for processing instructions. For example, the computerprocessor(s) may be one or more cores or micro-cores of a processor. Thecomputing system may also include one or more input devices, such as atouchscreen, keyboard, mouse, microphone, touchpad, electronic pen, orany other type of input device.

The computing system may be connected to or be a part of a network. Forexample, the network may include multiple nodes. Each node maycorrespond to a computing system, such as the computing system, or agroup of nodes combined may correspond to the computing system. By wayof an example, embodiments of the disclosure may be implemented on anode of a distributed system that is connected to other nodes. By way ofanother example, embodiments of the disclosure may be implemented on adistributed computing system having multiple nodes, where each portionof the disclosure may be located on a different node within thedistributed computing system. Further, one or more elements of theaforementioned computing system may be located at a remote location andconnected to the other elements over a network.

The node may correspond to a blade in a server chassis that is connectedto other nodes via a backplane. By way of another example, the node maycorrespond to a server in a data center. By way of another example, thenode may correspond to a computer processor or micro-core of a computerprocessor with shared memory and/or resources.

The nodes in the network may be configured to provide services for aclient device. For example, the nodes may be part of a cloud computingsystem. The nodes may include functionality to receive requests from theclient device and transmit responses to the client device. The clientdevice may be a computing system. Further, the client device may includeand/or perform all or a portion of one or more embodiments of thedisclosure.

Software instructions in the form of computer readable program code toperform embodiments of the disclosure may be stored, in whole or inpart, temporarily or permanently, on a non-transitory computer readablemedium such as a CD, DVD, storage device, a diskette, a tape, flashmemory, physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that, when executed by a processor(s), isconfigured to perform one or more embodiments of the disclosure.

While the above figures show various configurations of components, otherconfigurations may be used without departing from the scope of thedisclosure. For example, various components may be combined to create asingle component. As another example, the functionality performed by asingle component may be performed by two or more components.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A computer implemented method for assigningexecutable jobs to pipeline sets comprising: monitoring execution of aplurality of pipeline threads in a pipeline set to generate a pluralityof duty cycle metrics, wherein the plurality of duty cycle metricscomprises an amount of time that a corresponding pipeline thread isexecuting and actively processing data, and wherein monitoring executioncomprises monitoring a consumer queue of the corresponding pipelinethread to determine an amount of time that the data is in the consumerqueue of the corresponding pipeline thread; generating a weight for eachpipeline set of a plurality of pipeline sets to obtain a plurality ofweights, wherein generating the weight for the pipeline set comprisesdetermining the weight for the pipeline set based at least in part onthe plurality of duty cycle metrics; assigning a job request to a targetpipeline set selected, using the plurality of weights, from theplurality of pipeline sets; and processing the job request using atleast one pipeline thread of the plurality of pipeline threads in thetarget pipeline set.
 2. The computer implemented method of claim 1,wherein generating the weight further comprises: determining an averagenumber of data ingestion tasks assigned to the plurality of pipelinesets, wherein determining the weight is based on the average number ofdata ingestion tasks.
 3. The computer implemented method of claim 1,wherein generating the plurality of duty cycle metrics comprises:reading a plurality of timestamps and state information from a dutycycle ownership object, and generating, as a duty cycle metric of theplurality of duty cycle metrics, a weighted moving average using theplurality of timestamps and the state information.
 4. The computerimplemented method of claim 1, wherein generating the weight comprises:set, for at least a subset of the plurality of pipeline sets, a pipelineset duty cycle metric as a maximal duty cycle metric of the plurality ofpipeline threads in the pipeline set, generating a sliding windowaverage of incoming jobs to the plurality of pipeline sets, and usingthe sliding window average and the pipeline set duty cycle metric todetermine the weight for the pipeline set.
 5. The computer implementedmethod of claim 1, wherein generating the weight comprises: determining,for the pipeline set, a pipeline set duty cycle metric from theplurality of duty cycle metrics, generating a sliding window average ofincoming jobs to the plurality of pipeline sets, and when the slidingwindow average is equal to zero, calculating a sum of the pipeline setduty cycle metric across the plurality of pipeline sets, and determiningan assigned value for the pipeline set as a difference between the sumand the pipeline set duty cycle metric, normalizing the assigned valueacross the plurality of pipeline sets to obtain the plurality ofweights.
 6. The computer implemented method of claim 1, whereindetermining the weight for the pipeline set comprises: determining, forthe pipeline set, a pipeline set duty cycle metric from the plurality ofduty cycle metrics, generating a sliding window average of incoming jobsto the plurality of pipeline sets, and when the sliding window averageis greater than zero, assigning a value to the pipeline set usingperiodic update model to obtain an assigned value, and normalizing theassigned value across the plurality of pipeline sets to obtain theplurality of weights.
 7. The computer implemented method of claim 1,wherein determining the weight for the pipeline set comprises: sortingthe plurality of pipeline sets in increasing order of a plurality ofpipeline set duty cycle metrics to create an ordered list, wherein eachpipeline set duty cycle metric is determined from the plurality of dutycycle metrics for a corresponding pipeline set, generating a slidingwindow average of incoming jobs to the plurality of pipeline sets, andidentifying a maximal position in the order list in which a sum of apipeline set duty cycle metric at the maximal position minus a dutycycle metric of a subset of the plurality of duty cycle metrics up tothe maximal position is greater than or equal to the sliding windowaverage, the pipeline set duty cycle metric being in the plurality ofpipeline set duty cycle metrics, and setting an assigned value to zerofor each pipeline set in the ordered list that is at a position greaterthan the maximal position.
 8. The computer implemented method of claim1, wherein determining the weight for the pipeline set comprises:sorting the plurality of pipeline sets in increasing order of aplurality of pipeline set duty cycle metrics to create an ordered list,wherein each pipeline set duty cycle metric is determined from theplurality of duty cycle metrics for a corresponding pipeline set,generating a sliding window average of incoming jobs to the plurality ofpipeline sets, and identifying a maximal position in the order list inwhich a sum of a pipeline set duty cycle metric at the maximal positionminus a duty cycle metric of a subset of the plurality of duty cyclemetrics up to the maximal position is greater than or equal to thesliding window average, the pipeline set duty cycle metric being in theplurality of pipeline set duty cycle metrics, for each pipeline set ofthe plurality of pipeline sets that is at a position less than themaximal position in the ordered list, assigning the pipeline set anassigned value calculated as a function of a difference between a dutycycle metric of the pipeline set and a duty cycle metric at the maximalposition, a rate adjustment, and the sliding window average, andnormalize the assigned value across the plurality of pipeline sets toobtain the plurality of weights.
 9. A computing device, comprising: aprocessor; and a non-transitory computer-readable medium having storedthereon instructions that, when executed by the processor, cause theprocessor to perform operations including: monitoring execution of aplurality of pipeline threads in a pipeline set to generate a pluralityof duty cycle metrics, wherein the plurality of duty cycle metricscomprises an amount of time that a corresponding pipeline thread isexecuting and actively processing data, and wherein monitoring executioncomprises monitoring a consumer queue of the corresponding pipelinethread to determine an amount of time that the data is in the consumerqueue of the corresponding pipeline thread; generating a weight for eachpipeline set of a plurality of pipeline sets to obtain a plurality ofweights, wherein generating the weight for the pipeline set comprisesdetermining the weight for the pipeline set based at least in part onthe plurality of duty cycle metrics; assigning a job request to a targetpipeline set selected, using the plurality of weights, from theplurality of pipeline sets; and processing the job request using atleast one pipeline thread of the plurality of pipeline threads in thetarget pipeline set.
 10. The computing device of claim 9, whereingenerating the weight further comprises: determining an average numberof data ingestion tasks assigned to the plurality of pipeline sets,wherein determining the weight is based on the average number of dataingestion tasks.
 11. The computing device of claim 9, wherein generatingthe plurality of duty cycle metrics comprises: reading a plurality oftimestamps and state information from a duty cycle ownership object, andgenerating, as a duty cycle metric of the plurality of duty cyclemetrics, a weighted moving average using the plurality of timestamps andthe state information.
 12. The computing device of claim 9, whereingenerating the weight comprises: set, for at least a subset of theplurality of pipeline sets, a pipeline set duty cycle metric as amaximal duty cycle metric of the plurality of pipeline threads in thepipeline set, generating a sliding window average of incoming jobs tothe plurality of pipeline sets, and using the sliding window average andthe pipeline set duty cycle metric to determine the weight for thepipeline set.
 13. The computing device of claim 9, wherein generatingthe weight comprises: determining, for the pipeline set, a pipeline setduty cycle metric from the plurality of duty cycle metrics, generating asliding window average of incoming jobs to the plurality of pipelinesets, and when the sliding window average is equal to zero, calculatinga sum of the pipeline set duty cycle metric across the plurality ofpipeline sets, and determining an assigned value for the pipeline set asa difference between the sum and the pipeline set duty cycle metric,normalizing the assigned value across the plurality of pipeline sets toobtain the plurality of weights.
 14. The computing device of claim 9,wherein determining the weight for the pipeline set comprises:determining, for the pipeline set, a pipeline set duty cycle metric fromthe plurality of duty cycle metrics, generating a sliding window averageof incoming jobs to the plurality of pipeline sets, and when the slidingwindow average is greater than zero, assigning a value to the pipelineset using periodic update model to obtain an assigned value, andnormalizing the assigned value across the plurality of pipeline sets toobtain the plurality of weights.
 15. The computing device of claim 9,wherein determining the weight for the pipeline set comprises: sortingthe plurality of pipeline sets in increasing order of a plurality ofpipeline set duty cycle metrics to create an ordered list, wherein eachpipeline set duty cycle metric is determined from the plurality of dutycycle metrics for a corresponding pipeline set, generating a slidingwindow average of incoming jobs to the plurality of pipeline sets, andidentifying a maximal position in the order list in which a sum of apipeline set duty cycle metric at the maximal position minus a dutycycle metric of a subset of the plurality of duty cycle metrics up tothe maximal position is greater than or equal to the sliding windowaverage, the pipeline set duty cycle metric being in the plurality ofpipeline set duty cycle metrics, and setting an assigned value to zerofor each pipeline set in the ordered list that is at a position greaterthan the maximal position.
 16. The computing device of claim 9, whereindetermining the weight for the pipeline set comprises: sorting theplurality of pipeline sets in increasing order of a plurality ofpipeline set duty cycle metrics to create an ordered list, wherein eachpipeline set duty cycle metric is determined from the plurality of dutycycle metrics for a corresponding pipeline set, generating a slidingwindow average of incoming jobs to the plurality of pipeline sets, andidentifying a maximal position in the order list in which a sum of apipeline set duty cycle metric at the maximal position minus a dutycycle metric of a subset of the plurality of duty cycle metrics up tothe maximal position is greater than or equal to the sliding windowaverage, the pipeline set duty cycle metric being in the plurality ofpipeline set duty cycle metrics, for each pipeline set of the pluralityof pipeline sets that is at a position less than the maximal position inthe ordered list, assigning the pipeline set an assigned valuecalculated as a function of a difference between a duty cycle metric ofthe pipeline set and a duty cycle metric at the maximal position, a rateadjustment, and the sliding window average, and normalize the assignedvalue across the plurality of pipeline sets to obtain the plurality ofweights.
 17. A non-transitory computer-readable medium having storedthereon instructions that, when executed by a processor, cause theprocessor to perform operations comprising: monitoring execution of aplurality of pipeline threads in a pipeline set to generate a pluralityof duty cycle metrics, wherein the plurality of duty cycle metricscomprises an amount of time that a corresponding pipeline thread isexecuting and actively processing data, and wherein monitoring executioncomprises monitoring a consumer queue of the corresponding pipelinethread to determine an amount of time that the data is in the consumerqueue of the corresponding pipeline thread; generating a weight for eachpipeline set of a plurality of pipeline sets to obtain a plurality ofweights, wherein generating the weight for the pipeline set comprisesdetermining the weight for the pipeline set based at least in part onthe plurality of duty cycle metrics; assigning a job request to a targetpipeline set selected, using the plurality of weights, from theplurality of pipeline sets; and processing the job request using atleast one pipeline thread of the plurality of pipeline threads in thetarget pipeline set.
 18. The non-transitory computer-readable medium ofclaim 17, wherein generating the weight further comprises: determiningan average number of data ingestion tasks assigned to the plurality ofpipeline sets, wherein determining the weight is based on the averagenumber of data ingestion tasks.
 19. The non-transitory computer-readablemedium of claim 17, wherein generating the plurality of duty cyclemetrics comprises: reading a plurality of timestamps and stateinformation from a duty cycle ownership object, and generating, as aduty cycle metric of the plurality of duty cycle metrics, a weightedmoving average using the plurality of timestamps and the stateinformation.
 20. The non-transitory computer-readable medium of claim17, wherein generating the weight comprises: set, for at least a subsetof the plurality of pipeline sets, a pipeline set duty cycle metric as amaximal duty cycle metric of the plurality of pipeline threads in thepipeline set, generating a sliding window average of incoming jobs tothe plurality of pipeline sets, and using the sliding window average andthe pipeline set duty cycle metric to determine the weight for thepipeline set.